Societies that value the rights of the individual recognize that people are generally entitled to privacy. However, under certain circumstances, societies may have a compelling need to know about the locations and activities of individuals. In particular it would be advantageous to observe, track and/or identify individuals entering a private or secure location. It would also be advantageous to observe, track and/or identify individual planning to carry out a crime or an act of terrorism. It would also be advantageous to observe, track and/or identify lost, injured, or confused child or adult who does not have the means or competency to contact parents, guardians, other family members, or helping professionals. Lack of competency may reflect lack of maturity or cognitive impairment due to problems such as intellectual deficiency, brain injury, senile dementia, or Alzheimer's disease. In these cases, society has a legitimate interest in tracking and, perhaps, intercepting people.
Private and government agencies attempt to secure property, information, and people in order to prevent illegal acts against persons or property and, after such acts have been committed, to locate, identify, and apprehend perpetrators. The need for such security has grown dramatically in the twenty-first century. Increased numbers of private and public security personnel guard facilities and attempt to control access to an ever growing group of restricted areas. However, security personnel cannot be everywhere. Therefore, there is a pressing need for automatic systems that will operate in various locations around the clock, and when necessary alert security personnel.
Methods such as security cameras and automatic locking systems enable security personnel to monitor and limit access to many environments from a single location. However, such systems are typically limited to enclosed facilities that tend to keep private records.
Public and private organizations gather and store records for purposes of retrieving information at a later date. For example, telephone service providers keep records of telephone calls, including the telephone numbers of callers and recipients, the time the call was made, the duration of the call, charges associated with the call, and the like. Such data is captured, managed, stored, preserved, and retrievable upon request. Although the main purpose of keeping such records is usually billing, government and private agencies can also use such records in investigatory work. However, the utility of these records is limited because they contain a select and limited amount of information.
More extensive records and analysis are sometimes generated by retail corporations, such as Wal-Mart, which gather data about customers at check-out and online. Such data includes Social Security numbers, drivers' license numbers, credit card information, and a history of products purchased. This data is used mainly for marketing and inventory purposes, to target consumers who may be interested in particular products and to stock stores according to expected demand. Data concerning consumer purchasing is mapped across computer models to forecast future consumer behavior. This methodology has found, for example, that product consumption varies depending on geographic regions, cultural background, and time of year.
The first stage in the use of prediction models is the collection of appropriate data. From this data, patterns are deduced. Prediction models derive anticipated behaviors with degrees of confidence that are based on the regularity or variability of the targeted behavior. For example, retail stores stock certain merchandise for particular events based on forecasts obtained from the analysis of consumer behavior. In another context, cellular networks use prediction models to locate a mobile host (MH) the method facilitates the efficient allocation of bandwidth.
Prediction models are also used informally and formally by law enforcement agencies to forecast criminal behavior. Forecasting enables the agencies to efficiently allocate personnel. When processing means are employed, information technology personnel acquire and enter data concerning the nature and frequency of crimes committed in various neighborhoods. Analysis of this data enables them to forecast future events in these neighborhoods. More officers, or officers with special skills, can then be assigned to high-crime areas.
Government agencies are also developing systems for tracking and identifying individuals as well as events. Identification capabilities have increased with the introduction of biometric technologies. Every person possesses distinct and, typically, invariant biometric characteristics. Current biometric methods include DNA pattern recognition, body geometry feature recognition (ear, hand, finger, etc.), skin recognition (fingerprints, palmprints, etc.), facial recognition, optical recognition (retinal scan, iris scan, etc.), voice recognition, signature recognition, keystroke recognition, vascular pattern recognition, infrared identification (face, hand, hand vein, etc.), odor recognition, and the like.
Biometric systems commonly comprise verification and identification modes. Biometric verification systems are used in industry, Internet security, airport security, and the like. So-called “smart cards” can be fitted with memory chips that contain physiological data about the individual card holder. Then, in order to confirm the identity of the card holder, separate biometric device captures a sample of the individuals physiological data, for example, by scanning a fingerprint or the person's iris, and then compares the newly scanned data with data stored in the smart card. If the data from both sources matches, it is inferred that the individual bearing the card is the same as the individual whose data was stored in the card. Government agencies may implement such verification systems in mass transit environments, such as railways, seaports, and airports. Workers and visitors might be given a universal security card loaded with biometric data for authentication, in contrast to the plurality of individual cards we find in most present systems. However, though biometric verification systems may show that the person bearing the card is the same as the one whose data is stored in the card, they do not necessarily connect with any remote database to obtain the identity of the person or information as to whether or not the person is on, say, a watch list. The present invention, by contrast, advantageously endeavors to match newly captured biometric data with data stored in remote databases to determine the identity of the individual and make a background check.
Biometric personal identification systems rely on databases populated with biometric samples and the corresponding current identification of individuals. When a new biometric sample is captured, it is compared against one or more databases to identify the individual. Such identification systems use a biometric identifier or sensing device to capture data from an individual prior to permitting access to, for example, a venue, a facility, or a computer network. If the currently captured data corresponds with stored data, a signal can indicate whether or not the individual should be granted access to the location. Other uses of such systems include keeping records of individuals present in work environments, school environments, hotels, sporting events, or school buses. Security personnel at Super Bowls and holiday events have used facial recognition techniques to scan the crowd and compare currently-captured faces with faces stored in databases of criminals and missing children. However, biometric identification systems currently in use do not necessarily augment the pre-populated databases. Data which finds no match in the databases may well be discarded. Therefore, no running record is made of the appearances and activities of all scanned individuals. It would be advantageous, as in the present invention, to have the capacity to monitor the activity of any individual sensed by the system and create a record based on newly captured data.
Overall, the utility of current biometric verification and identification systems is limited in that these systems missing part of the “puzzle” that would make them exponentially more useful. What is needed is a system that can collect a plurality of biometric data on people in a plurality of locations, store and catalogue the data, and retrieve it at any time for purposes of analysis and prediction regardless of whether it matches data in a pre-populated database.